Prediction of Compressive Strength of Concrete Using Artificial Neural Network and Genetic Programming

Table 6

Architecture selected for model 2 (GP model).

Parameters	Values	Description
Initial population size	Dataset 1: 49 (without fly ash); Dataset 2: 27 (with fly ash)	Dataset is of two types. One is without any substitution of cement by fly ash and the second one is with 0.15 of the cement replaced by fly ash. Dataset 1 is of 49 tuples in total. Dataset 2 is of 27 tuples in total
Number of input parameters	04 (cement (C), water (W), fine aggregate (sand), coarse aggregate (CA)). 05 (cement (C), water (W), fine aggregate (sand), coarse aggregate (CA), 28-day compressive strength (CS28)), 06 (cement (C), water (W), fine aggregate (sand), coarse aggregate (CA), 28-day compressive strength (CS28), 56-day compressive strength (CS56)). Fly ash (FA) is used with dataset 2 only; all other parameters are the same as dataset 1	When output is 28 days, then the number of input parameters is 04; when output is 56 days, the number of input parameters is 05 as 28-day compressive strength is taken as input; when output is 91 days, the number of input parameters is 06 as 28-day compressive strength and 56-day compressive strength are also taken as input. In dataset 2, FA is replacing 15% of the cement
Function set	+, −, , /, sqrt	Set of functions used
Training percentage	75	—
Selection method	Tournament	—
Tournament size of replacement	3	—
Maximum generations	100000	Maximum number of iterations
Crossover	0.7	Probability of crossover
Mutation		Probability of mutation
Mu	100	Population size
Lamda	150	Number of children produced
Objectives	COD, RMSE	Coefficient of determination, root mean square error